As DNN-based applications use a tremendous amount of data, they require accordingly large memory and computation capacities. However, pervasive data-generating devices fail to deploy large-scale computation with reasonable energy consumption and latency. The current progress to adapt DNNs to mobile devices presents only unsatisfactory solutions: either to execute the classification on resource-constrained units using compressed models [1]} resulting in low accuracy, or to offload the data to be identified by larger models hosted in powerful servers.
The traditional wisdom resorts to remote
cloud servers to execute complex inference tasks [2]}.
However, the remote inference limits the ubiquitous deployment of deep learning. Indeed, such a server-centric approach cannot ensure the real-time requirements of services that need prompt intervention, such as car plates or accidents recognition.
To cater for the latency and bandwidth issues, DNN computation should be moved at the proximity of data-generating devices to make the system autonomous and reduce the required communication bandwidth.
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